Lecture_5_0_NetworkX_first_intro.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Networkx: a first introduction\n",
    "\n",
    "NetworkX is a python-based package for complex networks computing. It contains a wide set of algorithms for generating, analysing, and drawing graphs.  \n",
    "Information can be found on Git following the official page of the package:\n",
    "\n",
    "https://networkx.github.io/documentation/stable/index.html\n",
    "\n",
    "## Main features\n",
    "- Data structures for representing many types of networks, or graphs\n",
    "- Nodes can be any (hashable) Python object, edges can contain arbitrary data\n",
    "- Flexibility ideal for representing networks found in many different fields\n",
    "- Easy to install on multiple platforms\n",
    "- Online up-to-date documentation\n",
    "\n",
    "## When should I USE NetworkX?\n",
    "- Unlike many other tools, it is designed to handle data on a scale relevant to modern problems.\n",
    "- Most of the core algorithms rely on extremely fast legacy code\n",
    "- Highly flexible graph implementations (a graph/node can be anything!)\n",
    "- Extensive set of native readable and writable formats\n",
    "- Takes advantage of Python’s ability to pull data from the Internet or databases\n",
    "\n",
    "##  When should I AVOID NetworkX?\n",
    "- Large-scale problems that require faster approaches (i.e. massive networks with 100M/1B edges)\n",
    "- Better use of memory/threads than Python (large objects, parallel computation)\n",
    "\n",
    "\n",
    "## Installation  \n",
    "with pip you can install and upgrade, just type on the shell  \n",
    "`pip install networkx`  \n",
    "or, for upgrading  \n",
    "`pip install --upgrade networkx`  \n",
    "First of all import all the packages needed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By definition, a Graph $G$ is a collection of nodes (vertices) along with identified pairs of nodes (called edges, links, etc).  \n",
    "In NetworkX, nodes can be any hashable object e.g. a text string, an image, an XML object, another Graph,\n",
    "a customized node object, etc. (Note: Python’s *None* object should not be used as a node as it determines whether\n",
    "optional function arguments have been assigned in many functions.)  \n",
    "**Note** Lists are not allowed, as they are not hashable in python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "G=nx.Graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding Nodes  \n",
    "Nodes can be addedd manually or read from a list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "G.add_node(1)\n",
    "G.add_nodes_from([2,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Graphs can be easily visualised by typing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw(G)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another option is to add a *nbunch* of nodes. An *nbunch* is any iterable container of nodes that is not itself a node in the graph. (e.g. a list, set, graph, file, etc..). Even another graph works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#First I create a path graph\n",
    "H=nx.path_graph(10)\n",
    "nx.draw(H)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#then I include it in the graph\n",
    "G.add_nodes_from(H)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(nx.nodes(G))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NodeView((0, 1, 2, 3, 4, 5, 6, 7, 8, 9))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.nodes(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yIJhu933HTR8g49GdrBBxpj1cGl19x8dvgf7T32NVebIbK8vjO2kXmyesh7u78kCY6+sS4W8fLDPdtGQwKJey/X2xgsSZ9vFs7fXx+FjuOx+Nyir6Xq+sCTg8dIWDlSbOtNM00JPJ758YVlVlJibMwAKJM+3l0iiQlDiDS6NAMuIMAMm4lQoAkhFnAEhGnAEgGXEGgGTEGQCSEWcASEacASAZcQaAZMQZAJIRZwBIRpwBIBlxBoBkxBkAkhFnAEhGnAEgGXEGgGTEGQCSEWcASEacASAZcQaAZMQZAJIRZwBIRpwBIBlxBoBkxBkAkhFnAEhGnAEgGXEGgGTEGQCSEWcASOZ/bd1QDzTu4j4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw(G)\n",
    "\n",
    "#The edges!!!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph $G$ now contains $H$ nodes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "G.add_node(H)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now $G$ contains $H$ as a _node_. This flexibility is very effective as it allows graphs of graphs, graphs of files, graphs of functions and much more. It is worth thinking about how to structure your application so that the nodes are useful entities. Of course you can always use a unique identifier in $G$ and have a separate dictionary keyed by identifier to the node information if you prefer. \n",
    "\n",
    "*(Note: You should not change the node object if the hash depends on its contents.)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw(G)\n",
    "\n",
    "#The edges!!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(nx.nodes(G))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NodeView((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, <networkx.classes.graph.Graph object at 0x11a009650>))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.nodes(G)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding Edges\n",
    "\n",
    "As for nodes, edges can be added one by one or imported form a file, another graph etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#G can also be grown by adding one edge at a time,\n",
    "G.add_edge(1,2)\n",
    "e=(2,3)\n",
    "G.add_edge(*e) # unpack edge tuple*\n",
    "nx.draw(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    " G.add_edge(H,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAFCCAYAAADL3BUJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAEHpJREFUeJzt3T9snGmdwPHfHEHnGVgsI9kdSoUEhYeCGIqTUPZ00uEpbpVtQAikBOkKN1sgr3SuVruN0SpaJCjcnHQGAYJiwZUdrdCiFHeN3eChgS0QkRB3ThFZoEyK230pHjmJTf6N552Z3/j9fCQKJzPvPss6+fp95nmfp1VVVRUAQBr/MO0BAACniTMAJCPOAJCMOANAMuIMAMmIMwAkI84AkIw4A0Ay4gwAyYgzACQjzgCQjDgDQDLiDADJiDMAJCPOAJCMOANAMuIMAMlcmvYAgCEcHUVsb0ccHkYcH0fMz0d0uxE3bkQsLk57dEBNWlVVVdMeBPAc+/sRm5sRe3vl6wcPHv1eux1RVRGrqxEbGxErK9MZI1AbcYbstrYi1tcjBoMS4adptUqob96MWFub3PiA2pnWhsxOwnz//vNfW1Xldevr5WuBhpnlzhmy2t+PuHr1xcJ8VqcTcft2xJUrtQ8LGD+rtSGrzc0ylX0eg0F5PzCT3DlDRkdHEZcvn174Nay5uYg7d6zihhnkzhky2t4e/RqtVj3XASZOnCGjw8PR7pojytR2v1/PeICJEmfI6Pi4nuvcu1fPdYCJEmfIaH6+nussLNRzHWCixBky6nbLgq5RtNsRy8v1jAeYKKu1ISOrtaHR3DlDRktLEaurUbVa53t/qxXR6wkzzChxhqT+79vfjnPfN7fb5RAMYCaZ1mb8HHM4tH6/H71eL/7rS1+Kf7l1a7gtPDsdh1/AjBNnxscxh+fy61//Or72ta/F97///fj617/uVCpoIHFmPATlXH72s5/Fa6+9Fj//+c/j5ZdffvQbBwflB53d3fL/2eN7bp/8oNPrlR90HHYBM0+cqd8wxxyeuKhTsUNM6b/zzjvxve99L3Z3d2P5aY9A3b1brtfvlw1GFhbK41LXr/uIAC4QcaZejjkshpjS/+iLX4z19fV47733Ym9vLz7zmc9MZ8xAGuJMvV59NWJn59lT2U/TakVcuxbx7rv1j2uShpjSr9rt+M/PfS5+/MlPxs7OTizY0QsIcaZONs4415T+g499LD72zjvx8ddeG+PAgFniOWfq0/RjDvf3h/+sPSLmPvwwPr6xURZ9AYQ4U6emH3O4uXl6FfUwBoPyfoCIuDTtAXCBZDvmcJKbnxwdlcVf5/2UqKrKY1J3787ulD5QG3GmPlmOOXzWSulf/CLijTfq3/ykzin9118f/VrATDOtTX1qOOZwEBFv37oV3/nOd+K9996LB8NOk29tlUe5dnZKlM++fzAov7azU163tTXSeB9q+pQ+UCtxpj7Xr498ibm5ufjXn/40Pv3pT8dbb70VS0tL0ev14gc/+EF88MEH8cyHCx5fKf286eWqKq9bXx8p0FVVxR//+Mf439///tzXOKWuKX1gpnmUinrV/JzzvXv34le/+lXcunUrbt26FXNzc/HVr341VldX4+WXX45PfOIT5YUT2Pzkr3/9a/z2t7+Nw8PDODw8jN/85jfR7/ej0+nEjyPin//85+H/2Wd961sRP/rR6NeBuji4ZirEmXqNMZJVVUW/338Y6v39/fjyl78cq6ur8e97e/HS++9Hq4YfCj766KP4wx/+8DDCJ//705/+FJ///OfjC1/4QnS73eh2u7G8vByLi4sRb79dPsseZWq73Y54802fOZODg2umSpyp34T21v7LX/4S77//fvz3L38Zb/3whzHKp93/f+lS/Mc3vhH/88EH0e/3Y2FhIbrd7qkQf/azn41Ll56yhtIGLFwkDq6ZOqu1qd/JH9Ix/+F+6aWX4pVXXolXfve7qObmRgrjh1UV146P49+++91YXl4efhvNpaVyFzHKlH6vJ8xM3zA/XD++diNCoGtkQRjjsbZWpqivXSt3hO326d9vt8uvX7tWXjfKH+rDw2iNuFL6Hz/8MP7pU5+Kr3zlK+ff33pj4+//PV9Uu13eD9N0zl3uHgbaLne1cefM+Fy5Uj7HHfcxh1k2P1lZKTMA553SvwincTHb6tjlbtYPrklCnBm/xcXxLnLKsvlJxMSm9KF2drlLxbQ2s6+GzU+i3S5383WY5JQ+1KXpB9ckY7U2sy/zSulxT+lDXb75zYif/GT063hWvxamtZl9mVdKj3tKH+qSZe0GEWFam4vCSmkYTaa1G4gzF8TJSulOZ7j3WSkNRba1Gw3nM2cuFjsbwflkXrvRQO6cuVislIbzOVm70Wqd7/12uauVO2cuLiulYTgTON2NFyPOADwyoYNreDaPUgHwiF3uUnDnDMDfOzgoe2Xv7pYIP77n9sl5zr1eeQzRVHbtxBmAp7N2YyrEGQCS8SgVACQjzgCQjDgDQDLiDADJiDMAJCPOAJCMOANAMuIMAMmIMwAkI84AkIw4A0Ay4gwAyYgzACQjzgCQjDgDQDLiDADJiDMAJCPOAJCMOANAMuIMAMmIMwAkI84AkIw4A0Ay4gwAyYgzACQjzgCQjDgDQDLiDADJiDMAJCPOAJCMOANAMuIMAMmIMwAkI84AkIw4A0Ay4gwAyYgzACQjzgCQjDgDQDLiDADJiDMAJCPOAJCMOANAMuIMAMmIMwAkI84AkIw4A0Ay4gwAyYgzACQjzgCQjDgDQDLiDADJXJr2AABgqo6OIra3Iw4PI46PI+bnI7rdiBs3IhYXpzKkVlVV1VT+yQAwTfv7EZubEXt75esHDx79XrsdUVURq6sRGxsRKysTHZo4A9A8W1sR6+sRg0GJ8NO0WiXUN29GrK1NbHimtQFolpMw37///NdWVXnd+nr5ekKBducMQHPs70dcvfpiYT6r04m4fTviypXah3WW1doANMfmZpnKPo/BoLx/Atw5A9AMR0cRly+fXvg1rLm5iDt3xr6K250zAM2wvT36NVqteq7zHOIMQDMcHo521xxRprb7/XrG8wziDEAzHB/Xc5179+q5zjOIMwDNMD9fz3UWFuq5zjOIMwDN0O2WBV2jaLcjlpfrGc8zWK0NQDNYrQ0AySwtlb2yW63zvb/Viuj1JnIYhjtnAJpjRnYIE+cTCY8MA2AMhtlb+0SnM9HDL8Q58ZFhAIxJ8lOpmh3n5P9xABijg4Nyc7a7W/6ef3zP7ZObs16v3JxNYCr7cc2N8wxMawAwAXfvlo81+/2ywcjCQnlc6vr1qX2s2cw4z8iCAACaqZmPUs3IkWEANFPz7pxn6CF0AJqpeXfOM3RkGADN1Lw4z9CRYQA0U/PiPENHhgHQTM2L8wwdGQZAMzUvzjN0ZBgAzWS19nlYrQ3AGDXvznmGjgwDoJmad+ccYYcwAFJr3p1zRDld6ubNEtphnOytLcwAjNGlaQ9gak4Or3AqFQDJNHNa+3GJjwwDoJnE+UTCI8MAaCZxBoBkmrkgDAASE2cASEacASAZcQaAZMQZAJIRZwBIRpwBIBlxBoBkxBkAkhFnAEhGnAEgGXEGgGTEGQCSEWcASEacASAZcQaAZMQZAJIRZwBIRpwBIBlxBoBkxBkAkhFnAEhGnAEgGXEGgGTEGQCSEWcASEacASAZcQaAZMQZAJIRZwBIRpwBIBlxBoBkxBkAkhFnAEhGnAEgGXEGgGTEGQCSEWcASEacASAZcQaAZMQZAJIRZwBIRpwBIBlxBoBkxBkAkhFnAEhGnAEgGXEGgGTEGQCSEWcASEacASAZcQaAZMQZAJIRZwBIRpwBIBlxBoBkLk17ADPj6Chiezvi8DDi+Dhifj6i2424cSNicXHaowPgAmlVVVVNexCp7e9HbG5G7O2Vrx88ePR77XZEVUWsrkZsbESsrExnjABcKOL8LFtbEevrEYNBifDTtFol1DdvRqytTW58AFxIprWf5iTM9+8//7VVVV63vl6+FmgARuDO+Un29yOuXn2xMJ/V6UTcvh1x5UrtwwKgGazWfpLNzTKVfR6DQXk/AJyTO+ezjo4iLl8+vfBrWHNzEXfuWMUNwLm4cz5re3v0a7Ra9VwHgEYS57MOD0e7a44oU9v9fj3jAaBxxPms4+N6rnPvXj3XAaBxxPms+fl6rrOwUM91AGgczzmf1e1GvPvuaFPb7XbE8nJ9YwK4qGyN/ERWa59ltTbA+Nka+ZlMa5+1tFS+IVqt872/1Yro9YQZ4Gm2tspGTzs7Jcpnb4YGg/JrOzvldVtb0xjlVLlzfhI7hAGMxzBbI5/odBp3doE75ydZWSnfCJ3OcO87+QYSZoC/t78/fJgjHp1dcHAwnnElJM5Ps7b2KNDPm+JutRr5kx3AUGyN/MJMaz/PwUH5htjdLRF+/BvrZNFCr1cWLbhjBngyi22H4lGq57lypTxadfduWe7f75cNRhYWyuNS16834hsFYCR1bo38+uujXys5cX5Ri4uN+IYAGAtbIw/FZ84AjJ+tkYcizgCMn62RhyLOAIxft1sWdI2iQVsjW60NwPhZrT0Ud84AjJ+tkYfizhmAybA18gtz5wzAZNga+YV5zhmAyTnZ4nh9vTy3/KzJ21arLAJr4NbIprUBmDxbIz+TOAMwPbZGfiJxBoBkLAgDgGTEGQCSEWcASEacASAZcQaAZMQZAJIRZwBIRpwBIBlxBoBkxBkAkhFnAEhGnAEgGXEGgGTEGQCSEWcASEacASAZcQaAZMQZAJIRZwBIRpwBIBlxBoBkxBkAkhFnAEhGnAEgGXEGgGQuTXsA8EKOjiK2tyMODyOOjyPm5yO63YgbNyIWF6c9OoBataqqqqY9CHiq/f2Izc2Ivb3y9YMHj36v3Y6oqojV1YiNjYiVlemMEaBm4kxeW1sR6+sRg0GJ8NO0WiXUN29GrK1NbnwAY2Jam5xOwnz//vNfW1Xldevr5WuBBmacO2fy2d+PuHr1xcJ8VqcTcft2xJUrtQ8LYFKs1iafzc0ylX0eg0F5P8AMc+dMLkdHEZcvn174Nay5uYg7d6zivgis0qehxJlc3n474o03Rotzux3x5psRr79e37iYLKv0aTjT2uRyeDhamCPK1Ha/X894mLytrbLmYGenfC+c/X4YDMqv7eyU121tTWOUMFZWa5PL8XE917l3r57rMFlW6UNEuHMmm/n5eq6zsFDPdZic/f0XD/PjTgJ9cDCeccEUiDO5dLtlQdco2u2I5eV6xsPkWKUPD1kQRi5WazeT/+5wijtncllaKqtwW63zvb/Viuj1/AU9a7a3R79Gq1XPdSABcSafjY0yNX0e7XZ5P7PFKn04RZzJZ2WlHGLR6Qz3vk6nvM/WnbPHKn04xaNU5HTyWIxTqZrBKn04xZ0zea2tlUMsrl0ri33OTnW32+XXr10rrxPm2WWVPpxitTaz4e7dstin3y9TlwsL5S/i69ct/roIrNaGU8QZyOHVV8uWnOf5K6nVKjMo775b/7hgCsQZyME53vCQz5yBHKzSh4es1gbysEofIsK0NpDRwUHZK3t3t0T48T23T85z7vXKhjPumLmAxBnIyyp9GkqcASAZC8IAIBlxBoBkxBkAkhFnAEhGnAEgGXEGgGTEGQCSEWcASEacASAZcQaAZMQZAJIRZwBIRpwBIBlxBoBkxBkAkhFnAEhGnAEgGXEGgGTEGQCSEWcASEacASAZcQaAZMQZAJIRZwBIRpwBIBlxBoBkxBkAkhFnAEhGnAEgGXEGgGTEGQCS+RsYSn6LBikgawAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#by adding a list of edges,\n",
    "G.add_edges_from([(1,4),(1,3)])\n",
    "nx.draw(G)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As for nodes we can add any *ebunch* of edges. An *ebunch* is any iterable container of edge-tuples. An edge-tuple can be a 2-tuple of nodes or a 3-tuple with 2 nodes followed by an edge attribute dictionary, e.g. \n",
    "\n",
    "    (2,3,{‘weight’:3.1415}). \n",
    "\n",
    "Edge attributes are discussed further below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "G.add_edges_from(H.edges())\n",
    "nx.draw(G)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing edges or nodes\n",
    "\n",
    "Graphs can be demolished in a similar way by using: \n",
    "\n",
    "    Graph.remove_node()\n",
    "    Graph.remove_nodes_from()\n",
    "    Graph.remove_edge()\n",
    "    Graph.remove_edges_from()\n",
    "\n",
    "By example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We can remove some spefic set of nodes\n",
    "G.remove_nodes_from(H)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#or we can wipe out everything\n",
    "G.clear() # this removes everything!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw(G)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no problems when adding existing nodes or edges. For example, after removing all nodes and edges, NetworkX does not complain :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "G.add_edges_from([(1,2),(1,3)])\n",
    "G.add_node(1)\n",
    "G.add_edge(1,2)\n",
    "G.add_node(\"spam\") # adds node \"spam\". Yes, a node can be a string too...\n",
    "G.add_nodes_from(\"spam\") # adds 4 nodes: ’s’, ’p’, ’a’, ’m’, when the \"s\" really marks the difference :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NodeView(('a', 1, 2, 3, 'spam', 'm', 'p', 's'))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.nodes(G)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nodes and edges are not specified as NetworkX objects. All meaningful items can be used as nodes and edges. \n",
    "\n",
    "Common choices are numbers or strings, but a node can be any hashable object(except None), and an edge can be associated with any object X using: \n",
    "\n",
    "    G.add_edge(n1,n2,object=X) \n",
    "\n",
    "As an example, n1 and n2 could be names in the internet movie database, and x could refer to an XML record of characters played together with the intractions with other actors.\n",
    "\n",
    "You can also use \n",
    "\n",
    "    convert_node_labels_to_integers() \n",
    "\n",
    "and obtain a more traditional graph with integer labels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspecting the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 2, NodeView(('a', 1, 2, 3, 'spam', 'm', 'p', 's')))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NoN=G.number_of_nodes()\n",
    "NoE=G.number_of_edges()\n",
    "\n",
    "#We can examine them with\n",
    "NoN, NoE,G.nodes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EdgeView([(1, 2), (1, 3)])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.edges()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<dictionary-keyiterator at 0x11cf25f70>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neigh=G.neighbors(1)\n",
    "neigh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A odd way of presenting the result..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 3]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(neigh)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removing nodes or edges has similar syntax to adding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NodeView((1, 2, 3, 'spam'))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.remove_nodes_from(\"spam\")\n",
    "G.nodes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EdgeView([(1, 2)])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#otherwise you can remove a single specific edge:\n",
    "G.remove_edge(1,3)\n",
    "G.edges()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing edges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fast direct access to the graph data structure is possible using subscript notation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AtlasView({2: {}})"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G[1][2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can safely set the attributes of an edge using subscript notation if the edge already exists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "G.add_edge(1,3)\n",
    "G[1][3]['color']='blue'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AtlasView({2: {}, 3: {'color': 'blue'}})"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fast examination of all edges is achieved using *adjacency iterators*. Note that for undirected graphs this actually looks at each edge twice!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "FG=nx.Graph()\n",
    "FG.add_weighted_edges_from([(1,2,0.125),(1,3,0.75),(2,4,1.2),(3,4,0.375)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, {2: {'weight': 0.125}, 3: {'weight': 0.75}}),\n",
       " (2, {1: {'weight': 0.125}, 4: {'weight': 1.2}}),\n",
       " (3, {1: {'weight': 0.75}, 4: {'weight': 0.375}}),\n",
       " (4, {2: {'weight': 1.2}, 3: {'weight': 0.375}})]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(FG.adjacency())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each element is a tuple whose first element is the node and the second is a dictionary (of a dictionary) of its connections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2, 0.125)\n",
      "(2, 1, 0.125)\n",
      "(3, 4, 0.375)\n",
      "(4, 3, 0.375)\n"
     ]
    }
   ],
   "source": [
    "for n,nbrs in FG.adjacency():\n",
    "    # n and nbrs are respectively the node and the dictionary of dictionaries\n",
    "    for nbr,eattr in nbrs.items():\n",
    "        # nbrs is a the 'parent' dictionary\n",
    "        # eattr is again a dictionary\n",
    "        data=eattr['weight']\n",
    "        if data<0.5: print('(%d, %d, %.3f)' % (n,nbr,data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding attributes to graphs, nodes, and edges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Attributes such as weights, labels, colors, or whatever Python object you like, can be attached to graphs, nodes, or\n",
    "edges.  \n",
    "Each graph, node, and edge can hold key/value attribute pairs in an associated attribute dictionary (the keys must be\n",
    "hashable).   \n",
    "By default these are empty, but attributes can be added or changed using add_edge, add_node or direct\n",
    "manipulation of the attribute dictionaries named for a graph $G$:\n",
    "    \n",
    "    G.graph\n",
    "    G.node\n",
    "    G.edge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'day': 'Friday'}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G = nx.Graph(day=\"Friday\")\n",
    "G.graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Attributes can be modified later, as for dictionaries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'day': 'Monday'}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.graph['day']='Monday'\n",
    "G.graph\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "node attributes work the same way. Add node attributes work the same way:  \n",
    "\n",
    "    add_edge()\n",
    "    add_edges_from()\n",
    "\n",
    "subscript notation, or \n",
    "\n",
    "    G.edge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "G.add_edge(1, 2, weight=4.7 )\n",
    "G.add_edges_from([(3,4),(4,5)], color='red')\n",
    "G.add_edges_from([(1,2,{'color':'blue'}), (2,3,{'weight':8})])\n",
    "G[1][2]['weight'] = 4.7\n",
    "G[1][2]['weight'] = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, {2: {'color': 'blue', 'weight': 4}}),\n",
       " (2, {1: {'color': 'blue', 'weight': 4}, 3: {'weight': 8}}),\n",
       " (3, {2: {'weight': 8}, 4: {'color': 'red'}}),\n",
       " (4, {3: {'color': 'red'}, 5: {'color': 'red'}}),\n",
       " (5, {4: {'color': 'red'}})]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(G.adjacency())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: The special attribute *weight* should be numeric and holds values used by algorithms requiring weighted edges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Directed graphs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *DiGraph* class provides additional methods specific to directed edges, e.g. \n",
    "\n",
    "    DiGraph.out_edges()\n",
    "    DiGraph.in_degree() \n",
    "    DiGraph.predecessors() \n",
    "    DiGraph.successors() \n",
    "\n",
    "etc. To allow algorithms to work with both classes easily, the directed versions of neighbors() and degree() are equivalent to successors() and the sum of in_degree() and out_degree() respectively even though that may feel inconsistent at times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DG=nx.DiGraph()\n",
    "DG.add_weighted_edges_from([(1,2,0.5), (3,1,0.75)])\n",
    "nx.draw(DG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5, 1.25)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DG.out_degree(1,weight='weight'), DG.degree(1,weight='weight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(DG.successors(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(DG.neighbors(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graph generators and graph operations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**subgraph(G, nbunch)** - induce subgraph of $G$ on nodes in nbunch  \n",
    "**union(G1,G2)** - graph union  \n",
    "**disjoint_union(G1,G2)** - graph union assuming all nodes are different  \n",
    "**cartesian_product(G1,G2)** - return Cartesian product graph  \n",
    "**compose(G1,G2)** - combine graphs identifying nodes common to both  \n",
    "**complement(G)** - graph complement  \n",
    "**create_empty_copy(G)** - return an empty copy of the same graph class    \n",
    "**convert_to_undirected(G)** - return an undirected representation of $G$  \n",
    "**convert_to_directed(G)** - return a directed representation of $G$  \n",
    "\n",
    "There are also calls to generate the most common graphs, by example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DLBajf//+PDWoBo4ePYrx48cjMTGRV8X/z+XLl9G1a1ckJSXxOgg1wuXMyj2ZTIbz588jMDAQ+/fvh56enryQGzduLHQ8VkQPHjxAmzZtcODAAd4h619mz56NO3fuICgoSOgorIi4nFm5RES4ePEiAgMDERQUBB0dHYhEIohEIjRr1kzoeKyY8vPzYWdnB1dXV/z8889Cx1E52dnZMDc3x/Lly+Hq6ip0HFYEXM6s3CAiJCQkQCKRQCKRoHLlyhCLxRCJRGjZsqXQ8Vgp/Pzzz7h27RoOHz5c7q8zf8wff/yBQYMGITk5mbeKVQNczkyjEREuX74sL+QvvvhCXsimpqa8glUDHDlyBBMnTkRiYiJq1qwpdByVNm7cOMhkMvz+++9CR2GfweXMNA4R4erVq/JClkql8inrVq1acSFrkAcPHsDa2hohISFo37690HFUXnp6OkxMTODn5wd7e3uh47BP4HJmGuP69esIDAyERCJBdna2vJAtLS25kDVQXl4e7Ozs4O7ujhkzZggdR20cOnQI06dPR1JSEqpUqSJ0HPYRXM6sbKWmAjt3AklJQHo6oKMDmJkBI0YACthS8ebNm/JCTk9PlxdymzZtuJA13E8//YSbN2/i4MGDfJ25mMRiMRo0aIBly5YJHYV9BJczKxtxcYCPDxAWVvj7nJz//7sqVQAioHt3YPZswNq6WEPfuXNHXsgvXrxA//79IRKJYGNjwz+ky4lDhw5h8uTJfJ25hJ4/fw4zMzOEhYWhdevWQsdhH8DlzBRv82bgp5+A7OzCEv4YLa3Col65Ehg//pND3rt3D0FBQQgMDMTTp0/Rr18/iEQidOjQgQu5nElJSUGbNm0QGhqKdu3aCR1Hbe3atQtr167FxYsXUbFiRaHjsH/hcmaK9a6Ys7KK/p6qVT9Y0CkpKfJCTklJQd++fSEWi9GxY0d+DF45lZeXh06dOqF///6YPn260HHUGhHB2dkZnTt3Lvd7kKsiLmemOHFxgL198Yr5napVgdhYPDQ0xP79+xEYGIi7d+/Czc0NYrEYdnZ2qFChgsIjM/Uyffp03L59GwcPHuQ1BQpw//59WFlZ4ezZs2jSpInQcdg/cDkzxXF3B0JDPz2V/REyLS38oauLvgBcXV0hEonQpUsXnm4rTz6zePDgwYOYMmUKEhMTUaNGDaHTaoy1a9ciJCQEMTExfIlIhXA5M8VITQWMjN5f+FVM0ooVIf3rL3zFz0QuX4qweDCzUye4x8VhwbFjsLGxESanhpJKpejQoQNGjBiBsWPHCh2H/Q9/TGKKsXNnqYf4skIFfLV3b+mzMPWxeXPhpZDQ0MJS/veHu+xsICcHlcPDcSQjAzaXLgkSU5N9+eWX2Lp1K+bOnYvHjx8LHYf9D5czU4ykpFKdNQMo/EF89api8jDV98/Fg5+ZwPsSQMX8/MLXb96snHzliImJCSZMmIAJEyaAJ1NVA5czU4z0dMWM8+qVYsZhqi0urvir+oHC1//0ExAfXza5yrE5c+bg9u3b/FhJFcHlzBRDR0cx4+jqKmYcptp8fApnSkoiO7vw/UyhKlWqhG3btmHKlCn4+++/hY5T7nE5M8UwMwMqVy7dGFWqAKamisnDVFdqauHir5JOnxIBx44BaWmKzcXQrl07vodcRXA5M8UYPrzUQxTk5yNnwIDSZ2GqTQGLB6GlpZhx2H8sWbIEJ0+eRHh4uNBRyjUuZ6YYBgaFe2WXcGMI0tLCRX19NGrXDps2bUJubq6CAzKVwYsHVdo333yDLVu2YOzYscjIyBA6TrnF5cwUZ/bswqnpEtCqUgXtDx1CSEgIjhw5giZNmsDX1xf5+fkKDskEx4sHVZ6zszNsbW3h6ekpdJRyi8uZKY61deEe2VWrFu997/bWtrKCtbU1jh07hn379kEikaBZs2bYtWsXCgoKyiYzUz5ePKgW1qxZg3379uHChQtCRymXuJyZYo0fD6xcCapaFdLPvVZL66MPvWjfvj0iIiKwfft2bN++HS1btsTevXshlX52VKaCXr9+jeDgYIwfPx5Lw8JQwnXa/48XD5Y5PT09rFmzBqNHj0ZeXp7Qccod3r6TlYkr27bh2ZQp6CqVFj6g4J+3zbx7nnOPHoVT4VZWnxyLiBAdHQ1PT0+kp6dj/vz56Nu3L+8DrMLy8vJw/vx5REREICIiAteuXUOHDh3g5OQE59at0aJHD2iV5rpz5crAgweAvr7iQrP/ICK4uLigTZs2mDdvntBxyhUuZ1Ymhg8fDhMTE/w0bFjhqtqrVwuvEerqFp7xDB9e7B+sRIQTJ07A09MTeXl5WLBgAfr06cNPJ1IBRIQbN27Iy/jUqVNo3LgxnJyc4OTkhPbt26PyP2+1K8VDUqClBbi5AQcOKO4LYB/18OFDtG7dGrGxsWjRooXQccoNLmemcK9fv0aDBg1w+/Zt6JfBmQ0R4fDhw5g3bx4qVKgAb29vdO/enUtayZ4/f47IyEhEREQgMjISFSpUkJdxly5doKen9/E3l+LxogWVKqHC6dOfnXFhirNp0yb4+/vj1KlT/Cx1JeFyZgq3ceNGnDp1CgEBAWV6HJlMhpCQEHh5eUFbWxve3t5wdHTkki4jWVlZOHXqlLyMU1JSYG9vLy/kRo0aFe97/8+9tYtIWrkyvL7+Gq/EYqxater9s3FWZmQyGezs7CASiTB58mSh45QLXM5MoYgI5ubmWLNmDRwcHJRyTJlMBolEgvnz58PAwADe3t6wt7dXyrE1mUwmw+XLl+VT1efPn0erVq3QtWtXODk5wdraGhUqVCjdQd4VdHb2p6e4tbQK1yqsXInXAwfi+++/x82bNxEQEIDmzZuXLgMrkj///BO2trZISEiAkZGR0HE0HzGmQOfPn6eGDRuSVCpV+rHz8/Np9+7d1LBhQ+rSpQudPn1a6RnUXUpKCm3dupXEYjHp6elR06ZNadKkSXTw4EFKT08vm4PGxRG5uxNVrkxUpQpRYU0X/qpSpfDP3d0LX/c/MpmMfv/9d9LT06Pt27eTTCYrm2zsPYsWLSJnZ2f+fisBnzkzhRo9ejQaN26MmTNnCpYhPz8fu3fvxsKFC9GsWTN4e3ujTZs2guVRZW/evEFMTIz87PjVq1dwcHCQT1XXq1dPeWHS0oq9ePDatWsQi8UwMzPDli1bUK1aNeXlLYfy8/NhZWWFGTNmwMPDQ+g4Go3LmSnMmzdvYGRkhD///BOGhoZCx0FeXh62b9+OxYsXo1WrVvD29oaFhYXQsQSVn5+Pixcvyss4KSkJNjY28jI2NzdXu1vUsrKy8OOPPyIqKgoBAQGw4oViZSo+Ph49e/ZEcnJymSz4ZIW4nJnCbNmyBZGRkdi/f7/QUd6Tk5MDX19f+Pj4wMbGBgsWLIBpOdnAgohw69YteRnHxsbC2NhYXsa2traoUsItV1VNUFAQJk6ciFmzZmHq1Klq9yFDnfz000948uQJ9u7dK3QUjcXlzBTG0tISPj4+6Nq1q9BRPigrKwtbtmzB8uXLYWdnh/nz52vkYqK0tDRERUXJC5mI5GXs4OAAAwMDoSOWmb/++gsDBw5EzZo1sXPnTj6zKyNZWVkwNTXF+vXr0bNnT6HjaCQuZ6YQCQkJ6NevH+7evavyZywZGRnYsGEDVq9ejW7dusHLywuNGjUSOlaJ5eTk4PTp0/Iyvnv3Luzs7OSF3LRp03J1e1l+fj48PT3h7+8PPz8/dO7cWehIGikqKgojRoxAcnIyX+svA1zOTCHGjh2L+vXr45dffhE6SpG9efMG69atw7p169C7d294enrC2NhY6FifJZPJkJSUJC/jc+fOwdTUVF7Gbdu2RcWKFYWOKbjw8HAMHz4co0ePlm9YwxRr1KhRqFy5MjZu3Ch0FI3D5cxKLSMjA/Xr10dycjLq1KkjdJxie/36NVavXo2NGzeiX79+mDt3rnJXKRfB48ePERERgfDwcERFRUFHR0dexp07d4aOop70pGGePXuGIUOGICcnB3v37lW5/67q7tWrVzAxMUFgYCBsbW2FjqNRVHv+kamFgIAAdOrUSS2LGQCqV68Ob29v3Lp1CzVq1ECrVq0wadIkPHnyRLBMb9++xZEjRzBlyhS0aNEC5ubmCAsLQ+fOnXHhwgXcunULGzduhKurKxfzJ9SqVQsnTpxAz549YWVlhYMHDwodSaPo6upi/fr1GD16NHJK8yAT9h985sxKrU2bNpg/fz569OghdBSFSE1NxbJly7Bjxw4MHz4cM2fOLPNbwwoKChAfHy+fqr506RKsra3h5OSErl27wsLCQuWv5au6c+fOYdCgQejVqxdWrFjBW38qkLu7O1q0aIFFixYJHUVjcDmzUrl8+TJ69+6Nv/76S+M2xH/69Cl8fHywZ88ejB49GjNmzPj0wxyKgYhw9+5deRnHxMSgXr168qnqTp06oWrVqgo5Fvt/r1+/xujRo3H37l0EBASgadOmQkfSCE+ePIG5uTmioqJgZmYmdByNwOXMSmXixIkwMDCAl5eX0FHKzKNHj7B48WJIJBKMHz8e06dPh66ubrHHefny5Xu3OOXm5srL2NHREbVq1SqD9OzfiAi///475s6di1WrVmHo0KFCR9IIW7duxW+//YZz587x4jsF4HJmJZaZmYl69erhypUr5WKhzf3797Fo0SKEhoZi8uTJmDp16iev9+bm5uLs2bPyMr558yY6duwoL+QWLVqUq1ucVM3Vq1chFothaWmJTZs2QVtbW+hIao2I4ODggJ49e2L69OlCx1F7XM6sxHbu3In9+/fjyJEjQkdRqjt37mDhwoUICwvDjz/+iMmTJ+Obb74BESE5OVlexmfOnEHz5s3lZdyuXTt89dVXQsdn/5CZmYmpU6ciNjYWAQEBaN26tdCR1NqdO3dgY2ODCxcuoGHDhkLHUWtczqzE2rdvj1mzZqF3795CRxHEn3/+iVmzZiE6OhqNGzfG48eP8fXXX8vLuEuXLiWa/mbKFxAQgMmTJ2Pu3Ln44YcfeEajFFasWIHjx48jMjKSv4+lwOXMSiQ5ORndunVDSkpKubq+lJmZidjYWPnZ8ZMnT2BlZYUXL17g0aNH+OWXXzB27FheCayG7t27hwEDBqBWrVrYvn27whb/lTcFBQWwsbHBhAkTMHLkSKHjqC2+N4OViK+vL0aOHKnxxSyVSnHx4kUsXrwY9vb2qFWrFpYvXw49PT1s374daWlpCA8PR2JiIsLDwxEdHY1GjRph06ZNyM3NFTo+K4bvvvsOp0+fRtOmTWFhYYHY2FihI6mlChUqYOvWrZg1axaePn0qdBy1xWfOrNiys7NRr149xMfHo0GDBkLHUbi//vpLfmYcHR2NWrVqoWvXrvJbnL755ptPvj8uLg5eXl64du0a5s6di+HDh/N2mmomLCwMI0eOxNixY+Hp6alxtwkqw5w5c3Dr1i2Ve0qduuByZsXm7++PPXv2ICwsTOgoCvH69WtER0fLCzkjIwOOjo7yW5zq1q1bonHPnj0LLy8v3Lt3D/PmzcPgwYM1fqZBkzx9+hQeHh4oKCjAnj178O233wodSa3k5OTA3NwcS5cuhZubm9Bx1A6XMyu2Tp06YerUqXB3dxc6Sonk5eXh/Pnz8jK+du0aOnToIF/IZWpqqtCFLLGxsZg3bx6ePXsGLy8viMViPhNTE1KpFMuWLcP69evh6+sLFxcXoSOplT/++AMDBw7EtWvXUL16daHjqBUuZ1YsN27cQJcuXfDgwQO1maolIty4cUNexqdOnULjxo3lZdy+ffsyX8BFRIiKioKnpyfevHmD+fPno2/fvrwlp5o4c+YMBg0aBDc3NyxbtgyVKlUSOpLaGD9+PAoKCuDr6yt0FLXC5cyKZfr06ahUqRKWLFkidJRPev78OSIjIxEREYHIyEhUqFBBXsYODg6oWbOmILmICMePH8e8efOQl5eHBQsWoE+fPnzLiRp4+fIlRo8ejfv37yMwMBCNGzcWOpJaePPmDVq2bIndu3fzs7WLgcuZFVlOTg7q1auH8+fPq9wGA1lZWTh16pT87PjBgwewt7eXF3KjRo1UqgCJCIcPH5Y/Z9jb2xvdu3dXqYzsv4gImzdvhpeXF9asWQMPDw+hI6kiA3wJAAAgAElEQVSFw4cP48cff0RSUhLvGV9EXM6syPbt24ft27cjIiJC6CiQyWS4dOmSvIwvXryIVq1aycvY2tpaLRZfyWQyhISEwMvLC9ra2vD29oajoyOXtIq7cuUKxGIxbGxssGHDhs+u4GfAgAEDUL9+fSxfvlzoKGqBy5kVWZcuXTB+/Hj0799fkOOnpKS8d4uTnp6evIzt7e3Vem9kqVQKiUSC+fPnw9DQEN7e3rC3txc6FvuEzMxM/PDDDzh9+jQCAwPRqlUroSOptNTUVJiamuLYsWOwtLQUOo7K43JmRXLr1i107NgRDx8+VNr+0Onp6Th58iTCw8MRERGB169fv3eLkyY+bKOgoAB79+6Ft7c3jIyM4O3tjQ4dOggdi33C3r17MWXKFHh5eWHixIk86/EJu3fvxurVqxEXF6c2C0qFwuXMiuTnn38GgDKdksrPz8fFixflZ8dJSUlo166d/OzYzMys3Kxuzs/Px+7du7Fw4UI0a9YM3t7eaNOmjdCx2EfcuXMHAwYMwLfffott27YJtuBQ1RERnJ2dYW9vj9mzZwsdR6VxObPPysvLQ7169XDq1Ck0adJEYeMSEW7duiUv49jYWBgbG8PJyQldu3ZFhw4dUKVKFYUdTx3l5eVh+/btWLx4MVq1agVvb29YWFgIHYt9QF5eHmbPno2goCDs2bMHHTt2FDqSSrp//z6srKxw5swZNG3aVOg4KovLmX1WUFAQNm3ahJiYmFKPlZaWhqioKHkhE9F7tzgZGBgoILHmycnJga+vL3x8fGBjY4MFCxbA1NRU6FjsA44ePYpRo0Zh4sSJmDNnDm848wHr1q3DgQMHcPLkyXIzG1ZcXM7ss5ycnDBy5EgMHDiw2O/NycnB6dOn5WV89+5d2NnZyQu5adOmfI2uGLKysrB582asWLECdnZ2mD9/Ppo3by50LPYvjx8/lt9mtWfPHtSpU0fgRKpFKpWiQ4cOGD58OMaNGyd0HJXE5cw+6d69e2jbti0ePnxYpF20ZDIZkpKS5GV87tw5mJqaysu4bdu2vBBEATIyMrBhwwasXr0a3bp1g5eXFxo1aiR0LPYPUqkUS5YswcaNG7Ft2zb07NlT6Egq5dq1a7C3t8elS5d43/IP4HJmnzRnzhzk5ORg9erVH33No0eP5GUcFRUFHR0deRl37twZOjo6Skxcvrx58wbr1q3DunXr0Lt3b3h6esLY2FjoWOwfTp06hcGDB6N///7w8fFR2t0O6mD+/PlITEzEwYMHeQbtX7ic2Ufl5+ejfv36iI6Ofm/q9O3btzh58qS8kNPS0uDg4CAvZCMjIwFTl0+vXr3CmjVrsHHjRvTr1w9z587VyFvN1NXff/+NUaNG4fHjx9i3bx/PcvxPbm4uWrdujXnz5kEsFgsdR6XwlXj2UUeOHEHjxo3RuHFjnDt3Dt7e3ujYsSPq1KmDtWvXok6dOvD390dqaioCAwMxevRoLmaB6OrqwtvbG7du3UKNGjXQqlUrTJo0CU+ePBE6GgNQs2ZNhISEYOjQoWjXrh327dsndCSVUKlSJWzduhVTp07F33//LXQclcJnzuw/iAh3795Fnz59UKVKFdy9exf169eXnxl37NiR98dVcampqVi2bBl27NiB4cOHY+bMmTA0NBQ6FgNw6dIlDBgwAB06dMCvv/6Kr7/+WuhIgpsyZQpev36NXbt2CR1FZXA5MwCF027R0dHyqers7Gy8fv0amzdvRvfu3VGrVi2hI7ISePr0KXx8fODv748xY8ZgxowZ0NPTEzpWuZeRkYFJkybhwoULCAgIgLm5udCRBJWRkQETExP89ttv6Natm9BxVAKXczmVm5uLs2fPysv43fac786O9+3bh/T0dKxfv17oqEwBHj58iCVLlkAikWD8+PGYPn06dHV1hY5V7vn5+WHatGlYsGABxo8fX64XRZ04cQLjxo3D1atX+UEi4HIuN4gIycnJ8jI+c+YMmjdvLi/jdu3ayVeRFhQUwMjICMePH+eNLjTM/fv3sWjRIoSGhmLy5MmYOnUqr6YX2K1btzBgwAA0aNAA27ZtK9cfmoYOHYoaNWpg7dq1QkcRHJezBnvy5AkiIyMRERGByMhIVK1aVV7GXbp0+egPgUOHDmHp0qU4e/askhMzZblz5w4WLlyIsLAw/Pjjj5g8eTKfrQgoNzcXM2fORGhoKPbs2VNuH3by999/w8TEBCEhIbCxsQFSU4GdO4GkJCA9HdDRAczMgBEjAH19oeOWLWIaIyMjg44ePUpTp06lli1bkq6uLvXt25e2bNlCd+/eLfI4vXr1ou3bt5dhUqYqbty4QQMGDCADAwNasWIFZWZmCh2pXDt48CAZGBjQ4sWLqaCgQOg4gti3bx+Jv/uOCnr3JqpcufAX8P+/qlQp/DM3N6KLF4WOW2b4zFmNSaVSJCQkyKeqExISYGlpKT87trS0LPa+vg8fPoS5uTkePnzIq0jLkeTkZMyfPx9nz57FzJkzMXbs2CLtCMcU79GjRxg8eDAqVqwIPz8/1K5dW+hISkWbNyN38mR8JZV++l5fLS2gShVg5Upg/HhlxVMaLmc1c+/ePXkZx8TEoHbt2vIy7tSpU6mnJhcsWIDU1FRs3LhRQYmZOrl06RLmz5+PhIQEzJkzB6NGjUKlSpWEjlXuSKVSLFq0CFu2bMGOHTvg7OwsdCTl2LwZ+OknICur6O+pWlUjC5rLWcW9evXqvVucMjMz5WXs6Oio0A31pVIpjI2NcejQIbRq1Uph4zL1ExcXBy8vL1y/fh1z587FsGHDeE90AcTGxsLDwwMDBgzA4sWLNXvrz7g4wN6+eMX8TtWqQGwsYGWl8FhC4XJWMXl5eTh//jzCw8MRERGBGzduoEOHDvJCNjExKbPbLY4dO4b58+fj4sWLZTI+Uz9nz57FvHnz8Ndff2HevHkYPHgwKlSoIHSscuXFixcYMWIEnj9/joCAAHz33XdCRyob7u5AaGjhleXi0tIC3NyAAwcUn0sgXM7vCLQqkIhw48YN+ZnxqVOn0KRJE3kZt2/fXmnTiq6urujVqxdGjx6tlOMx9REbGwtPT088f/4cXl5eEIvF/JxiJSIirF+/HosWLcKGDRs0bx/q1FTAyAjIySn5GJUrAw8eaM4qbmHWoamQixcLV/0pcVXgs2fPyN/fn4YNG0Z16tShBg0a0JgxY0gikdCLFy8UdpziePz4MVWvXp3evn0ryPGZ6pPJZBQREUE2NjbUokULkkgkJJVKhY5VrsTHx1OjRo1o9OjRmrWyftmy//78Le6vKlWIli8X+itRmPJ95vxu8UF29qenUkq5KjArKwunTp2Snx0/ePAA9vb26Nq1K5ycnNCwYUPBdwZavHgxHjx4gN9++03QHEz1ERGOHz+OefPmIT8/HwsWLEDv3r0F/zdcXrx9+xYTJkxAYmIiAgICNGOjIA8PYM+e0o8zZAiwe3fpx1EFAn84EM6mTURVqxbvk1nVqoXv+wypVErx8fHk4+NDXbp0oW+++YZsbW1pwYIFdPbsWcrPz1fCF1h0UqmUGjRoQPHx8UJHYWpEJpPRwYMHydzcnCwtLeno0aMkk8mEjlVu7Ny5k/T09GjLli3q/33v1at0Z83vfvXqJfRXojDl88y5DFYFpqSkyM+Mo6KioK+vL79ubG9vD21tbcVkLwPh4eGYNWsWEhMThY7C1JBMJkNISAi8vLygra0Nb29vODo68pm0Ety8eRNisRiNGzeGr68vqlevLnSkYiMivHZxge7Ro6UfTIPOnMvn85x9fAqnsksiOxvw8UF6ejpCQ0MxceJENGnSBNbW1oiOjoazszMuX76MGzduYP369XBxcVHpYgaA33//Hd9//73QMZia+uKLL9C3b19cuXIFP/zwAyZNmgQ7OzucPHlS6Ggar2nTpjh//jxq164NCwsLnD9/XuhIRSKTyXDu3DnMmDEDjRs3xpYzZ5BX2gWGVaoAmjDF/z/l78xZAasCc7W00KxqVTRu315+dmxmZoYvvlC/zzrPnz9Hs2bNkJKSgmrVqgkdh2mAgoIC7N27F97e3jAyMsLChQvRvn17oWNpvIMHD+L777/HtGnTMGPGDJX7eZSfn4/Y2FgEBwcjNDQUNWrUgJubG9zd3dGqTh1oNWjAq7X/SdBJdSEoYFVgwVdfUd6SJUJ/JQqxdOlSGjlypNAxmAbKy8ujrVu3kpGREXXr1o0uXLggdCSN9+DBA7K1tSUnJyd6+vSp0HEoKyuLQkNDaejQoVSjRg1q06YN+fj40J9//vnfF7u5EWlpleznspYWkbu78r/AMqRaH62UISmpdJ/OAHyZl4eKN24oKJBwZDIZfH19eUqblYmKFSti1KhRuHXrFlxdXdG3b1+4uLjg0qVLQkfTWPXq1UNMTAxsbGzQunVrhIeHKz1Deno69u7di379+qFWrVpYt24drK2tceXKFVy4cAGzZs1C06ZN//vG2bMLp6ZLokqVwvdrEqE/HSgdrwqUi4qKIjMzM/Vf6cnUQnZ2Nq1fv55q165Nbm5ulJSUVPxBnj8vnP0aPLjw/8HBgwt/n5qq+MBqLjo6murWrUszZ86kvLy8Mj3W8+fP6ffffydnZ2fS1taWP9kuLS2teANt2kQFlSqVyV006qb8lfPgwYop5yFDhP5KSk0sFtOvv/4qdAxWzmRmZtLKlSvJ0NCQRCIRXb9+/fNvEmCzIE2QmppKPXr0IBsbG7p3755Cx75//z6tWbOGOnbsSDo6OiQWiykwMJDevHlT4jHfvn1Lv9SsSfmVKn1+iltLS2OLmag8lrMCrjnnVqhAT378UeivpFRSU1NJR0eHXr16JXQUVk69ffuWfHx8SF9fnzw8POj27dsffuG7PQnK+Q/rkpJKpbR69WrS19cniURS4nFkMhldu3aNFi1aRK1btyY9PT0aOXIkHTlyhLKzsxWSddKkSTRs2DCiuLjCa8iVKxd++PrQhzF398LXaSherV0C+V9+CWtDQ0BfH0OGDMHAgQMV+nQoZVi1ahWuXr2KnTt3Ch2FlXNv3rzBunXrsG7dOvTu3Ruenp4wNjYu/Et+hKDCxMfHY8CAAXB0dMSaNWtQpQjXd4kI8fHxCA4ORkhICDIzM+UrrG1tbRX6EJTY2FgMHjwYV69eha6ubuEfpqUVPvPg6lXg1StAV7fwdqnhwzVnVfbHCPzhQBgKWBUolUopOjqaRowYQdWrVycnJyfavXu3WuxNLZPJqEmTJnT69GmhozAm9/LlS/L09KQaNWrQ999/T88OHy7+Ln7/vA6pwWdVJZWenk4DBw6kli1bUnJy8gdfk5+fTzExMTR58mSqV68eNWnShGbNmkUXL14ss/UpGRkZ1LBhQzp06FCZjK+Oymc5X7yo0P/ps7KyKCAggHr27Ek6Ojrk4eFBx48fp4KCAoG+wE87efIktWjRgheCMZX04sULmjlzJh2uWJGkJb38pIG31iiKTCaj7du3k56eHvn6+pJMJqOcnBw6cuQIjRw5kvT19al169a0cOFCunbtmlJ+TkyZMoU8PDzK/DjqpPxNa79TRtNlqampCAgIgJ+fHx4/foyBAwdiyJAhMDc3V5ntDD08PGBlZYWpU6cKHYWxD0tNBdWvD63c3JKPoWmbUihYfHw8+vbtCyJCeno6zM3N4ebmBjc3NzRo0EBpOU6dOoUBAwbg6tWrqFGjhtKOq/IE/nAgrDJeaHLjxg2aM2cO1a9fn0xMTGjZsmX08OHDMvpiiubFixeko6Mj2KMpGSsSfoRgmUhLS6Pt27dTr169SFtbm5ycnMje3p7q169P58+fV3qezMxMatSoEYWGhir92Kqu/G1C8k/jxxc+xMLNrfBT9r8WSMgqVUKOlhbI1bXwdcVcYNKsWTMsXrwYf/31FzZu3Ijbt2/DzMwMDg4O2LlzJ96+favIr6ZI/Pz80KtXL9SsWVPpx2asyBSwWRCyswsXEpVzjx49woYNG9ClSxc0bNgQR48excCBA/Hw4UOEh4cjJiYGa9asgYuLC1asWAGZTKa0bHPnzkWbNm3Qp08fpR1TXZTfae1/+8CqQDI1RcetW7F02zbY2toq5DA5OTk4fPgw/Pz8EBsbi549e2LIkCFwcnJS6MrHDyEimJiYYNOmTbCzsyvTYzFWKi4uwJEjpR+nVy/g8OHSj6Nmbt26hZCQEAQHB+POnTvo1asX3N3d4eTkhKpVq37wPSkpKRg0aBC0tbWxe/duGBgYlGnGM2fOoH///rh69SqfLHyIwGfuKm/p0qU0ZsyYMhk7LS2NNmzYQG3btiVDQ0OaMmUKxcfHl9kCjNOnT1OTJk14IRhTfbxZULHIZDJKTEwkT09PatmyJdWuXZvGjx9PERERxdodLD8/n3755ReqU6cORURElFnerKwsatKkCR04cKDMjqHu+Mz5Mx49egQzMzM8fvy4SPcFltStW7fg7+8Pf39/VK5cGUOGDMHgwYNRv359hR1j2LBhMDMzw/Tp0xU2JmNlYvlywMurVFPbeV9+iZSRI9Fg40ZUrFhRgeFUg1Qqxblz5+T3IH/xxRdwd3eHu7s72rZtW6qnUkVFRWHo0KEYPnw45s+fr/Dv34wZM/Dw4UMEBAQodFxNwuVcBE5OThgzZgxEIlGZH4uIcPbsWfj5+SEoKAimpqYYMmQI+vXrBx0dnRKP++rVKxgbG+P27dvQ59WrTNUpYLOgggoV0NPUFPEpKejRowdcXV3h7OyMr7/+WoFBlSsvLw8xMTEIDg7GwYMHYWhoKN8UxNTUVKF3hKSmpmLo0KF48+YN9u3bByMjI4WMe+7cObi7uyMpKYl/Fn2KoOftasLPz4969Oih9OPm5OTQgQMHyNXVlapVq0YikYgOHz5cok3sf/31VxKLxWWQkjHFS0tLo7h69ahAAfc5P3r0iDZu3EhOTk6kra1NLi4utG3bNkpVk4dlZGRk0IEDB2jw4MGkq6tL7dq1o+XLl398u1MFkkqltGLFCtLX11fIFHRWVhY1bdqUgoKCFJBOs3E5F0FGRgbp6OgI+nzUFy9e0ObNm6l9+/akr69PkyZNogsXLhTp+rFMJiNTU1OKiopSQlLGSk4mk5Gfnx8ZGhrSqgEDSPbvfZVLuUPYq1evaM+ePdS/f3/S0dGhjh070qpVq+ju3bsCfLUf9/LlS9q9e7f8g7mjoyNt3LiRHj9+LEieCxcukLGxMY0fP56ysrJKPM7PP/9M/fv3V2AyzcXlXETDhg2j1atXCx2DiIju3LlD8+fPp4YNG1KTJk1o4cKF9Ndff3309efPn6eGDRuSVCpVXkjGiunevXvUrVs3MjMzo4vvni71bi+C4hZzEfYkyM7OpqNHj9Lo0aPJwMCAzMzMaN68eZSYmCjIosknT57Q5s2b5Wf4ffr0oV27dtHff/+t9Cwf8vr1axKLxWRqalq0J4n9y/nz58nQ0JCeP39eBuk0D5dzEUVFRVGrVq2EjvEemUxG586dowkTJpCenh7Z2trSb7/9Ri9fvnzvdSNHjqSlS5cKlJKxT8vPz6eVK1dSzZo1aenSpf+9bKOEp1IVFBTQ6dOn6aeffqKGDRuSkZERTZkyhWJiYig/P19BX+l/3b17l1auXEnt27en6tWr06BBgygoKEhl9+iXyWTk6+tLenp6tG3btiJ/iMnOzqYWLVpQQEBAGSfUHFzORSSVSqlevXp05coVoaN8UG5uLoWGhlLfvn2pWrVq1LdvXwoNDaW0tDSqXr06PXv2TOiIjP1HQkICtW7dmhwcHD59DVWJjxCUyWR09epVWrhwIVlaWlLNmjVp2LBhFBISQpmZmaUeOykpiRYsWEDm5uZkYGBAY8aMobCwMMrJySl1dmVJTk6mli1b0sCBAyk9Pf2zr589eza5u7vzbZzFwKu1i+GXX35BXl4eVqxYIXSUT3r16hX2798PPz8/XLp0CQYGBvD394eNjY3K7O/NyresrCx4eXlh9+7dWLZsGYYNG1a0f5sCPELwwYMHOHToEEJCQhAfH48uXbrA1dW1yDvtyWQyXLx4Ub4pSH5+vnyFdfv27fHll1+WSe6ylp2djWnTpiEiIgIBAQGwsrL64Ovi4uLQq1cvJCUlwdDQUMkp1ZjQnw7UyY0bN6h27dplOs2lSDKZjJo3b07Dhg2jJk2aUKNGjWj+/Pl0584doaOxcuzEiRNkbGxMgwYNUrvrj3///Tft3r2b3N3dqVq1amRvb09r166l+/fvv/e6vLw8ioyMpAkTJlCdOnWoefPm9Msvv1BCQoLGnT0GBQWRvr4+rVq16j/rWnJycqhly5a0d+9egdKpLz5zLqa2bdtiwYIFcHZ2FjrKZ8XHx6N///64e/cutLS0EBcXB39/fwQEBKBx48bw8PCAWCzmJ8EwpUhLS8O0adNw+vRpbNq0Cd27dxc6UqlkZ2cjIiICoaGhOHz4MOrWrYsWLVogIyMDZ86cQcOGDeVPeWrWrJnQccvU/fv3MXDgQOjq6mLXrl3y+5fnzp2La9euITg4mGftiql8P/iiBIYOHYrdu3cLHaNIfH19MXr0aHzxxRfQ0tJCmzZtsH79ejx+/BizZ8/GyZMnYWxsDDc3NwQHByO3NI/nY+wjiAh+fn4wNTWFgYEBkpOT1b6YAaBKlSqwt7eHk5MTOnXqhNu3b+PUqVM4c+YMvvnmG3Ts2BG2trZo3Lix0FHLXIMGDfDHH3/A3NwcFhYWiImJQUJCAnx9fbF582Yu5hLgM+dievHiBRo1aoQHDx6gWrVqQsf5qLdv36J+/fq4du0a6tSp89HXpaenY//+/fD390dSUhL69+8PDw8PdOjQgf+HYqV27949jBs3DmlpafD19f3odUl1kpqaikOHDiE4OBinT59Gp06d4Obmht69e0NfXx9EhKSkJISGhiI0NBSPHz+Gi4sL3Nzc4ODgUKbbAKuC8PBwDB8+HFKpFMuXL8ewYcOEjqSehJxTV1eurq60bds2oWN80u+//06urq7Fek9KSgotWbKEmjdvTsbGxuTp6Um3bt0qo4RMk+Xn59Py5cupZs2atGzZshLtaqdKUlJSaO3atdSpUyf5bn0BAQFFWqn8119/0dq1a8nOzk5+J4Wfn99/bnnUJNOmTZPf3pmSkiJ0HLXE5VwCISEhZGdnJ3SMT7K2tqajR4+W6L0ymYzi4+NpypQpZGBgQG3btqVff/2V0tLSFJySaaL4+HiysLAgR0dHtV58eP36dVq8eLH8dqoRI0bQoUOHKDs7u8RjpqWl0Y4dO6hPnz6kra1NDg4OtGHDBnr48KECkwsrMTGR9PX16eHDh7Rs2TIyMDCgkJAQoWOpHS7nEsjNzSU9PT26d++e0FE+6NKlS1SvXj0qKCgo9Vj5+fl09OhRGjhwIFWrVo1cXFxIIpGU6gcU00wZGRk0bdo0MjAwoF27dqndqmSZTEZxcXE0Z84catasGdWtW5cmTZpE0dHRZXKHRkZGBgUHB9PQoUOpRo0aZGVlRYsWLaLk5GS1+969k5ubS+bm5rRr1y75n507d44aNGhAkyZN4p8bxcDXnEto0qRJMDQ0hKenp9BR/mPixIkwNDTEvHnzFDrumzdvEBwcLL9/um/fvhgyZAhsbW1L9Xg6pv5OnDiBcePGoUOHDlizZo3aPG1IKpXi9OnTCAkJQUhICCpVqiR/7KKVlZXS/l3n5+fLc4SGhqJy5cpwdXWFq6srbGxs1Ob/rwULFiAuLg6HDx9+b83K69evMWbMGNy+fRuBgYFo2rSpgCnVhNCfDtTVhQsXqFGjRir3CTcjI4N0dXXLfJrs3ZSViYkJGRkZ0Zw5c+jGjRtlekymelJTU2nw4MFkbGxMx48fFzpOkeTk5Mj31NbX1ycLCwvy9vZWmTNWmUxGCQkJ5OnpSaampmRoaEhjxoyhY8eOqfQuYpcvXyZ9fX169OjRB/9eJpPRb7/9Rnp6erRz506V+F6rMi7nEpLJZNS0aVM6e/as0FHes337durVq5fSjieTyejSpUs0bdo0qlWrFllZWdG6devUbnMJVjwymYx27txJBgYGNH36dMrIyBA60ie9ffuWJBIJDRw4kKpXr062tra0atUqlb009U937tyhlStXkq2tLeno6JBIJKK9e/fS69evhY4ml5eXRxYWFrR9+/bPvvbq1avUokUL8vDwoDdv3ighnXrici6FxYsX07hx44SO8Z527drRoUOHBDl2fn4+HT9+nDw8PEhHR4d69uxJ+/btK9Uj5pjquXPnDjk6OpKFhQXFx8cLHeejXrx4QTt27CAXFxfS1tambt260ZYtWwR99GtpPX/+nLZu3Uq9evUibW1t6tq1K23evFmwR0m+s3DhQnJ2di7y2XBmZiaNGTOGGjVqpNL/hoTE5VwKKSkpVKNGDZWZakpKSqK6deuqxPaib9++pd27d5OTkxNVr16dRowYQdHR0fzYSjWWl5dHy5Yto5o1a9KKFStU4t/Zvz169Ig2bNhAXbp0oWrVqpG7uzv5+fnRq1evhI6mcG/evKGgoCAaPHgw6erqUtu2bcnHx4f+/PNPpeZISkoiPT09evDgQbHfGxAQQPr6+rRmzRqe5v4XXhBWSg4ODhg/fjz69esndBT88MMPqF69Ory9vYWO8p4nT55g79698Pf3x8uXLzF48GB4eHigZcuWQkdjRRQfH4/Ro0fDwMAAW7ZswXfffSd0JLnbt2/LHypx69Yt9OrVC+7u7ujatSuqVq0qdDylyMvLwx9//IGQkBAcPHgQ2tra8gVl1tbWZbagLD8/HzY2NpgwYQJGjRpVojHu3buHgQMHwsDAADt27ICenp6CU6opoT8dqLudO3eSi4uL0DEoKyuLatSo8Z8N+FVNUlISzZgxg+rUqUMWFha0evVqtZ5m1HRv376lH3/8kQwNDcnPz08lzm7erXOYN28emZiYUK1atWjcuFsavDwAACAASURBVHF04sQJys3NFTqe4KRSKV28eJHmzJlDLVq0oNq1a5fZ92fx4sXUtWvXUv+7yM3NpRkzZtC3335LJ0+eVFA69cblXEpv3rwhHR0dwRdA7d69m5ydnQXNUBwFBQUUERFBQ4cOJR0dHXJ2dqY9e/ao/MKi8uTYsWNkZGREQ4YMEXwDGqlUSqdPn6bp06eTsbExGRsb0/Tp0+nMmTN8qeQzbt68ScuWLaN27dpR9erVaeDAgRQYGFjqxVjJycmkp6en0B3Ajh8/TrVr1yYvLy+VvGyiTFzOCuDh4UHr1q0TNIOtrS0FBwcLmqGkMjIyaM+ePeTs7Ew6Ojo0dOhQioiIUMgmKqz4nj9/TgMHDqTvvvuOwsPDBcuRm5tLJ06coHHjxlGtWrXI1NSU5s2bR5cvX1aJM3h19OTJE/rtt9/I2dmZtLW1qUePHvT777/Ts2fPijVOfn4+WVtb02+//VYmGR0cHKhTp04atXNacXE5K0B4eDhZWloKdvzr169TrVq11H7/YiKip0+f0urVq8nCwoLq1q1LM2bMoCtXrggdq1yQyWS0fft2MjAwoBkzZlBmZqbSM2RmZlJwcDANGTKEdHV1ycbGhpYtW8Z7vJeB9PR0CggIoAEDBlD16tWpffv2tHz5crp9+/Zn37t06VJydHQssw9JBQUFtGTJEjI0NBTs7hOh8YIwBZBKpTAyMsKJEycEWeQ0bdo0VK5cGUuWLFH6scvStWvX4O/vjz179kBXVxdDhgzBoEGDPvmULVYyd+7cwdixY/H69Wts3boVFhYWSjv269evceTIEYSEhCAyMhLW1tZwc3ODq6sr6tatq7Qc5Vlubi5iYmIQGhqKgwcPombNmvIFZZaWlu/t9nX9+nXY2dkhLi4ODRo0KNNcZ8+exaBBg+Dq6oply5ahUqVKZXo8lSL0pwNNMXPmTPr555+Vftzs7GzS09Oju3fvKv3YyiKVSik6OppGjBhB1atXJycnJ9q9eze9fftW6GhqLy8vj3x8fKhmzZq0cuVKpV3ne/r0KW3ZsoW6du1K2tra1Lt3b9qxYwe9ePFCKcdnHyeVSuncuXM0c+ZMatKkCX377bc0ceJEioyMpKysLGrTpg1t3rxZaXlevnxJ7u7uZGFhQTdv3lTacYXG5awgycnJVKdOHaVfJ927dy85Ojoq9ZhCysrKooCAAOrZsyfp6OjQ4MGD6fjx4+V+8UhJXLhwgczMzKhbt25K2Snr3r17tGrVKurQoYN8YZJEIuEPWSru+vXrtGTJEmrTpg1VrVqVDA0NSSKRKHXxpkwmo02bNpGenh7t3r1baccVEpezAllaWip9AY29vT1JJBKlHlNVPH/+nNatW0dWVlZUu3ZtmjZtGl26dIkXC33G27dvacqUKWRoaEj+/v5l9v2SyWSUnJxM3t7e1KpVK9LX16fRo0fT0aNHVWbjHlZ0N27coOrVq9OCBQvIycmJtLW1ycXFhbZt20apqalKyXDlyhVq1qwZDR06VOM/1HE5K9C6devIw8NDace7efMmGRgY8L2dVPiD45dffiEjIyNq2bIlLV26tFyv9PyYo0ePkpGREQ0bNqxMppClUimdP3+eZs6cSY0bN6Z69erRlClTKDY2llffq7GCggJq164dbdiwQf5nr169oj179lD//v1JR0eHOnbsSKtWrSrzS2wZGRk0cuRIatKkCSUmJpbpsYTE5axAqamppKOjo7TN3GfMmEEzZsxQyrHUhVQqpdjYWBo9ejTp6upSly5daMeOHeV+g/1nz56RWCym7777jiIiIhQ6dn5+PkVFRdGkSZOobt261KxZM5ozZw7Fx8fzLIaGWLVqFdnZ2X30nvLs7Gz5k74MDAzIzMyM5s2bR4mJiWX2b2Dv3r2kr69P69ev18h/Z1zOCta7d2/auXNnmR8nJyeHDAwMytUCieLKzs4miURCLi4uVK1aNRowYAAdPXq0XF2flslktG3bNtLX16eZM2cq7Pao7OxsOnToEA0fPpxq1qxJlpaWtHjxYrp+/bpCxmeq4+bNm//X3p0HRXWlbQB/WjQCUQmCSsWtjCsZGtBI3JdIHCfu4BZHVGKCDoL6qWghLgmKUSNxxS24EBHHFdByGVPGREZxwVHoxm0Gg4orCIg6gNL0+f7oAdlUltu3m+b5VVEppfvct610P33Pfe85wsbGRiQlJZXr8RqNRpw5c0b4+fmJ1q1bi5YtW4oZM2aI3377TfL3XlJSkujcubMYOnSoyTUTMpwlduDAAfHZZ5/p/Th79+6V5TimIi0tTYSEhIguXbqIJk2aiBkzZpj8md2///1v0bdvX9G5c2dx5cqVKo+XlZUl/v73v4vRo0cLKysr0bt3b7FmzRqjXzKWKk+j0YgePXqIdevWVer5Wq1WqNVqsWTJEvHJJ58IGxsbMXHiRBEVFSXZF8WXL1+K2bNni+bNm4uYmBhJxjQGDGeJ5ebmyrLG9eeffy52796t12OYqps3b4qFCxeKVq1aCXt7e7F06VKTCphXr16JpUuXChsbG7Fq1aoqna2kpqaKrVu3ikGDBhWuKBUaGmrw5WpJHqtXrxa9evWSbInUO3fuiPXr1xfuGjZ8+HARFhYmyVnv0aNHhZ2dnVi8eLFJ9DdwERI98Pb2RvPmzREQEKCX8W/duoWuXbsiJSUF5ubmejlGTSCEQGxsLMLDw7F//34olUqMHz8eI0eOhJWVlaHLq5QLFy7Ay8sLTZs2xaZNmyq1SERKSkrhLk9XrlzBgAED4ObmhoEDB1bbfxequKSkJHTt2hXnzp1D27ZtJR8/IyMDR48eRXR0NE6ePIlOnTrBzc0Nw4YNQ8uWLSs15oMHD+Dh4QEhBHbt2lW9F7Ex7HcD0xQbGyvat2+vtylTf39/MWvWLL2MXVPl5uaKgwcPiuHDh4sGDRqIUaNGicOHD1ebJVGfPXsmpk2bJuzs7MTu3bsr/P/e9evXxffffy86d+4sbGxshKenpzh06JDIzs7WU8VkzPLz80WvXr3E6tWrZTledna2OHTokPjqq6+Era2t6NixowgMDBQJCQkV/n9Zo9GIJUuWiCZNmogjR47oqWL945mzHggh0L59e+zatQuffvqppGPn5eWhRYsWOHXqFOzt7SUdm3TS09Oxf/9+hIeH4z//+Q/GjBmD8ePHw8XFpdgyhsbiyJEjmDp1KlxdXREcHAwbG5t3PkcIgStXriAyMhKRkZHIysqCm5sb3Nzc0Lt3b9SpU0eGyslYrV+/Hnv37sXp06dhZmYm67E1Gg1iY2MRHR2NqKgo1KpVq3Ap0e7du5e7njNnzmDcuHEYMWIEli9fjvfee6/sB6amAmFhgEoFZGUBVlaAoyPw1VdAo0bSvbAKYjjrSVBQEB49eoSQkBBJx42MjMSaNWsQExMj6bhUtlu3bmHXrl0IDw+HmZkZPDw84OHhgVatWklzgCp8MDx69AgzZszA5cuXsWXLFvTr1++tj8/Pz8fZs2cRFRWFqKgo1KlTB+7u7nB3d4eLiwtq1aolzWuiau3WrVvo0qULYmNj0a5dO4PWIoSASqVCdHQ0oqOjcf/+fQwZMgRubm5wdXWFhYXFW5+fkZGBr7/+GikpKdizZw/atGnz+pdxccCyZcDx47o/5+a+/p2FBSAE8MUXwLx5gIuLHl7dOxjwrN2kJScnC1tbW8lXQhowYIAIDw+XdEx6N61WK86dOyemTp0qbG1tRc+ePcWWLVtERkZG5Qa8eFEINzchzM11P7qPAt2PhYXu79zcdI8ro5bQ0FDRqFEj4e/v/9ap59zcXHHs2DHh5eUlGjduLJycnERgYKBQq9Um3alOlZOfny/69OkjgoODDV1KmZKTk8Xq1atFnz59RIMGDcSIESNEeHj4W9+HWq1WhISECFtbWxEREaH7y40bhbC0FEKhKP7eK/mjUOget3GjTK/wNYazHvXp00fSPZaTk5OFjY0NrwMa2MuXL0V0dLQYMWJE4QdEVFRU+Vdqq8IHw82bN0WfPn2Ei4uLiI+PL3P458+fi/3794u//vWvhVsBBgcHm/TmKCSNkJAQ0bVr12rR7ZyWliZ27Nghhg0bJurXry9cXV1FSEjIG1cGvHLlimjfvr34uVs3obWwePt7r+SPAQKa4axH27ZtE8OHD5dsvAULFojp06dLNh5VXUZGhvjpp59Er169hI2NjfD29haxsbFvPistCOYKfjDkrV8vgoKChI2NjVizZk2pD8/09HQRFhZW+EHVv39/sWnTJvHgwQMZ/hXIFPzxxx/CxsZGXL9+3dClVNiLFy9EZGSkmDBhgmjYsKHo3LmzCAoKEomJicXei//9/XeRa2ZWsfdf0YCOi5PtNfGasx49e/YMLVq0QFJSEmxtbas0lkajKdwz2sHBQaIKSUrJycmIiIhAeHg4tFpt4fXp1q1b6x4QFwf07QtkZ1d47ByFAv7dumHW7t2Ft5k8ePAA0dHRiIyMRFxcHFxdXeHm5obBgwfD2tpawldGpk6r1eLzzz/HF198gTlz5hi6nCrJy8vDmTNnEBUVhejoaJibmxc2lHVbuRKKQ4d0cVtRCgXg5gYcPCh90WUdjuGsX+PGjUP37t3h4+NTpXEOHz6M5cuXIzY2VqLKSF+EEIiLi8OuXbuwZ88etG3bFh4eHvj66FG8d+xYpT4YtAoFFMOH49YPPxTeg3zz5k0MGjQIbm5uGDBgAN5//309vBqqCTZv3oywsDCcPXtW9u5sfRL/uyshOjoaMQcO4B/Xr6NKK0OYmwN378rSxc1w1rMTJ05g0aJFuHDhQpXGGTx4MEaOHAlPT09pCiNZ5OXl4cSJEzgUGor1hw9X6YPhpUKBTra26OXuDjc3N3z22Wdvvj2EqJxu374NFxcXxMTEmPbtmT/8AO2iRaj18mXlx7CwAAIDARlmF3jvhJ65uroiJSUFN27cqPQYKSkpiI2NxahRoySsjORQp04dDB48GKE9eqBuFVdzq12nDtSzZ2Pz5s0YMGAAg5mqTAiBb775BrNnzzbtYAYAlapqwQwAOTmAWi1NPe/AcNaz2rVrY9y4cdi5c2elx9i+fTvGjh3LacvqTKWCouh9lJVg9uoVal29KlFBREBoaCiysrLg5+dn6FL0LytLmnEyM6UZ5x0YzjKYMGFCYZNQReXn52Pbtm2YPHmyHioj2VSzDwYyfXfv3sX8+fOxY8cO1K5d29Dl6J9U68LL1GzJcJaBUqmEra0tfv/99wo/98SJE7Czs4OTk5P0hZF8qtkHA5k2IQS8vLwwc+bMmnP3h6OjrqGrKiwsAKVSmnregeEsk4kTJ1Zqavunn37iWbMpqGYfDGTatm/fjvT0dMydO9fQpcjH0xNV7n4WApCpKZfd2jJ5/PgxOnTogHv37pX72vGDBw/g4OCAu3fvol69enqukPQqNRVo2bL4+r0VJeNtHGS6UlJS0KlTJ5w6dQrKGvRl78aNG3jSuze6paWhUjeLyXyfM8+cZdKkSRP06NEDkZGR5X7Ojh07MGrUKAazKWjcWLeIfmV3tVIogIEDGcxUJUIITJ48GdOnT68xwfzixQv4+/ujZ8+eSPHwQC1Ly8oNZGGh2wRDJgxnGU2YMKHcU9tarRahoaGc0jYl8+bp3uCVIfMHA5mmsLAwPHr0CP7+/oYuRe+EENi3bx/s7e1x//59qNVqjF21CorgYKCiAW1pCQQHA50766fYssi2UCiJ7OxsYW1t/caF2Yv6xz/+ITp16iRDVSSrSq6tbYhdcci03Lt3T9ja2r5xwxRTcu3aNeHq6iqUSqWIiYkp/YBqsCsVz5xlZGFhgZEjRyIiIuKdjw0NDYWXl5cMVZGsvL1138AtLd89xa1QvP7G7u0tT31kkoQQmDJlCnx9fU36zo/nz59j7ty56N27N4YMGYLLly+jV69epR/o7Q2cPq27hmxuXnpGy8JC9/dubrrHGeD9x4YwmZ09exaTJ09GYmIiFG/4cH706BHs7e1x584dNGjQQOYKSRaXLuk2ej92TBfCOTmvf1ew0fvAgbqpbDmn0sgk7dy5E6tWrcLFixdNcmU5IQT27t0LPz8/uLq6YsWKFbCzsyvfk9PSgLAw3cpfmZm62xWVSl1XtgF7PBjOMhNCoE2bNti3bx8++eSTMh+zfPlyJCUlYevWrTJXR7Iz0g8GMh0PHjyAs7MzTpw4gY4dOxq6HMldvXoV06ZNQ3p6OjZs2ICePXsauiRJMJwNIDAwEOnp6Vi3bl2p32m1WrRr1w4RERHo0qWLAaojIlMhhMDQoUPRsWNHLF682NDlSOr58+cIDAzEzz//jEWLFsHb29ukVjoznVdSjYwfPx5du3ZF8Ny5eG/3bkCl0i3vaGWFWxYWaFa3Lj799FNDl0lE1VxERATu3LmDgzLdmysHIQT27NmDOXPmoH///khMTESTJk0MXZbkGM4G8FF6Ovbn58Pso48AM7NiC1O0MDPDSYUCihEjdNcbXVwMWCkRVVcPHz7ErFmzcPz4cZO5zpyYmAhfX19kZWVh37596N69u6FL0ht2a8tt0yagb1/0ysyEWV5eqRWj6ubno7ZGA0RHA3376h5PRFQBQgh4e3tj8uTJb+xtqU6ePXuGWbNmoV+/fhg1ahQuXbpk0sEMMJzltWkT4OcHZGej1rsu9QsBZGfrHs+AJqIK2LNnD5KSkrBw4UJDl1IlQghERETA3t4eWVlZSExMhI+PD8zMKrUAZ7XChjC5xMXpzoSzsyv+XEtL3b12vKWGiN7h8ePHcHR0xJEjR+BSjS+LqdVq+Pr64vnz59iwYQO6detm6JJkxTNnuSxbVvxe1orIydE9n4joLQqmsydNmlRtgzkrKwv/93//B1dXV3z55ZeIi4urccEMMJzlkZoKHD+um6quDCF0i1WkpUlbFxGZlH379uHGjRv49ttvDV1KhQkhEB4eDnt7e7x48QJXr16Ft7d3jZjCLgu7teUQFlb1MRQK3Thz5lR9LCIyOampqZgxYwYOHToE86ruHS6zhIQE+Pr6IicnB1FRUVzjATxzlodKVbV9fAHd1LZaLU09RGRyfH19MXHixGoVbE+fPsX06dPRv39/eHh44MKFC9Wqfn1iOMshK0uacTIzpRmHiEzK/v37oVKpEBgYaOhSykWr1eLnn3+Gvb09cnNzce3aNUyZMqXGTmGXhdPacrCykmSYNI0G72dnw7Kym4UTkclJS0vD9OnTERkZWS2ms+Pj4+Hj44NXr17h0KFDXA3xDXjmLAdHR932Y1Xw0swMu1Qq2NjYoEOHDhgzZgyWLVuGY8eO4f79++AdcUQ107Rp0zBu3Dij72h++vQppk2bhgEDBmDixIk4f/48g/kteJ+zHFJTgZYtq3bd2dwcuHsXeR98gBs3biAhIQHx8fGF/wUAJycnODs7w8nJCU5OTrC3t0edOnUkehFEZGwOHjyIgIAAxMfHw6LknsRGomAKOyAgAMOGDcPSpUthY2Nj6LKMHsNZLu7uuiU5K/PPrVDoNv1+w+L1Qgg8fPiwWGAnJCTgzp07aN++fbHAdnJyQsOGDav4YojI0J48eQKlUokDBw6gR48ehi6nTJcvX4avry/y8/OxYcMGdOZCSuXGcJaLAVYIy87ORmJiYrHAVqlU+OCDD4qdZTs7O+Ojjz5CrVq8ykFUXYwbNw5NmjTBqlWrDF1KKZmZmViwYAEOHDiApUuXYtKkSfx8qSCGs5yKrK1dbpaWQHAw4O0tSQlarRbJycnFAjs+Ph4ZGRlQKpXFAtvBwQHvv/++JMclIulER0djzpw5SEhIMKoGUa1Wix07dmD+/Plwd3dHUFAQZ+oqieEst4KAzsl5+xS3QgFYWEgazG+TmZlZGNYFgX3jxg20aNGicDq8ILg//PBDKBQKvddERKVlZGTAwcEBe/fuRa9evQxdTqF//etf8PHxAQBs2LDBJHbDMiSGsyFcuqRbK/vYMV0IF11z28JCF9oDB+r2czbgNZq8vLzC5rOi17O1Wm2xM2w2nxHJZ/z48WjYsCHWrl1r6FIA6L4szJ8/H1FRUfj+++/h6enJKWwJMJwNKS1NtySnWq1bYMTaGlAqAU9PoFEjQ1dXpqLNZ0UD+/bt2+jQoUOpjnFOaRFJ5/Dhw5g5cyZUKpXBLzlptVps27YNCxYswKhRo7BkyRJYW1sbtCZTwnAmSRQ0nxUN7LKaz5ycnNC6dWt+syaqoMzMTDg4OGD37t3o06ePQWu5dOlS4b7KGzZsQMeOHQ1ajyliOJPeFDSflbwnu2TzmZOTE5RKpcHPBIiMmaenJ+rVq4eQkBCD1ZCeno6AgAAcPnwYy5Ytw4QJE/hFW08YziS7zMxMqFSqYh3j169fR/PmzYsFtrOzM5vPiAAcPXoU06ZNg0qlQr169WQ/fn5+PrZu3YpFixZhzJgxWLx4MT744APZ66hJGM5kFPLy8nDz5s1iZ9glm88KArtDhw547733DF0ykSyePn0KBwcH7Ny5E/369ZP9+BcvXoSPjw/q1q2LkJAQODs7y15DTcRwJqMlhMCjR49KBXbBymclb/Fi8xmZokmTJsHc3BwbN26U9bhPnjzBvHnzcOTIEaxYsQLjx4/nLJaMGM5U7RRtPisI7aLNZ0UDm81nVJ0dP34cU6dOhUqlQv369WU5Zn5+PkJDQ7Fo0SKMHTsWgYGBnMI2AIYzmYSizWdFz7LT09OhVCqLBTabz6g6yMrKgoODA8LCwuDq6irLMc+fPw8fHx9YWlpiw4YNcHR0lOW4VBrDmUxaQfNZ0cAuaD4rub44m8/ImHh5eaFWrVrYsmWL3o+VlpYGf39/HD9+HCtWrICHhwffCwbGcKYap6D5rOQtXlqttsxtN9l8RnL75Zdf4OXlBbVajQYNGujtOPn5+diyZQu+++47jBs3Dt999x2srKz0djwqP4YzEV43n5UM7Dt37qBdu3altt3kfrSkL8+ePYNSqURoaCj+/Oc/6+04586dg4+PD+rXr4+QkBAolUq9HYsqjuFM9BbZ2dm4evVqscBWqVSwsrIqdYsXm89IClOmTIFWq0VoaKhexk9NTYW/vz9OnDiBlStXYuzYsZzCNkIMZ6IK0mq1uH37dqlbvIo2nxUENpvPqCJOnjyJSZMmQa1WSz69rNFosHnzZgQGBmLChAn49ttv9TplTlXDcCaSyLuaz4pez27atCnPVqiY58+fQ6lUYvPmzfjLX/4i6dhnz56Fj48PrK2tERISgj/96U+Sjk/SYzgT6VHR5rOC0C7afFZy2002n9VcU6dORW5uLrZv3y7ZmI8fP8bcuXPx66+/Ijg4GGPGjOGXwmqC4Uwks6LNZ0XPspOTkwtXPit6PZvNZ6bv1KlTmDhxItRqtSQLfmg0GmzcuBFLliyBp6cnFi1aJNsiJiQNhjORkSjZfFbwY2VlVeqebDafmY4XL17A0dER69evx6BBg6o83j//+U/4+vrCxsYGISEh+PjjjyWokuTGcCYyYgXNZyVv8UpPT4eDg0OpbTcNsWMRVY2vry9evHiBsLCwKo3z6NEjzJ07F6dOncKPP/6I0aNHcwq7GmM4E1VDT58+LbbtZnx8PK5fv45mzZqVusWLzWfG6/fff4eHhwfUajWsra0rNYZGo0FISAiCgoIwadIkLFy4kFPYJoDhTGQiNBpNmdtuajSaUoHN5jPD++9//wtHR0esWbMGQ4YMqdQYMTEx8PX1RePGjbF+/XrY29tLXCUZCsOZyMSVte1m0eazotez2XwmnxkzZiAjIwPh4eEVfu7Dhw8xZ84cnD59GqtWrcLIkSM5O2JiGM5ENVBOTk7htpsFga1SqdCgQYNSgd2mTRs2n0ksJiYGX375JRITEyu0D3leXh5CQkKwdOlSfPPNN1iwYAH7DEwUw5mIAJTdfJaQkIAnT57AwcGh1LabDIXKyc7OhqOjI3788UcMGzas3M87ffo0fHx88OGHH2L9+vVo3769HqskQ2M4E9FblWw+S0hIwLVr19CsWbNSt3ix+ezdZs6cidTUVERERJTr8Q8ePICfnx/Onj2LVatWwd3dnf/GNQDDmYgqrGTzWcHZtkajKRXYbD577cyZMxg9ejTUavU7r+/n5eVh7dq1WL58OaZMmYKAgACu016DMJyJSDJlbbuZnJxc5rabtra2hi5XVtnZ2XB2dsby5cvh7u7+1sf+9ttv8PX1RfPmzbFu3Tq0a9dOpirJWDCciUivcnJyytx2s379+mVuu2lmZmbokvXCz88P9+7dw549e974mPv372P27Nk4f/48Vq9ejeHDh3MKu4ZiOBOR7IQQZW67mZaWVua2m9W9+Sw2NhYjRoyASqVCo0aNSv3+1atXWLNmDX744Qf87W9/Q0BAACwtLQ1QKRkLhjMRGY2C5rOigV20+azo9exmzZoZz1llaioQFgaoVEBWFmBlBTg6Al99hZx69dCxY0cEBQVh5MiRpZ7666+/wtfXF61atcLatWvRtm1b+esno8NwJiKjVtB8VvJadkHzWdHA/vjjj+VtPouLA5YtA44f1/05N/f17ywsACGgbt4cu1u0wLKTJ4s99d69e5g1axYuXryItWvXYujQocbzZYMMjuFMRNVSyeazhIQE/PHHH2jXrl2pbTf10ny2aRPg5wfk5ABv+RjNB1DLwgKKH38EvL3x6tUrrF69GitXrsTUqVPh7+/PKWwqheFMRCajZPNZwU/9+vXL3Haz0s1nBcGcnV3+51ha4vrXX8Ptl1/QunVrrF27Fm3atKnc8cnkMZyJyKSVbD4rONtOS0srtu1muZvP4uKAvn0rFsz/k61QIG7lSvSeNYtT2PRWDGciqpGysrJKbbt57do1NG3atNQtXsWaz9zdgejot05lv4lQKKBwcwMOHpT41ZCpYTgTEf1P0eazotezX716BWdnZ/Ro2xbf7tiB2nl5lT+IuTlw9y5Qxi1Ve+/+OAAAAfpJREFURAUYzkRE71DQfFZ37Vp0++UX1M3Pr/xgFhZAYCAwZ450BZLJqW3oAoiIjJ2dnR3s7OyA8HCgKsEM6Lq71WppCiOTxU1aiYjKKytLmnEyM6UZh0wWw5mIqLysrKQZx9pamnHIZDGciYjKy9FR19BVFRYWgFIpTT1kstgQRkRUXqmpQMuWxZfprCh2a1M58MyZiKi8GjcGvvgCqOwCIgoFMHAgg5neiWfOREQVUYUVwmBpCZw+DXTuLHlZZFp45kxEVBEuLkBwsC5oK8LSUvc8BjOVA+9zJiKqKG9v3X/LsSsVFApdE1hw8OvnEb0Dp7WJiCrr0iXdfs7HjulCOCfn9e/+t58zBg4E5s3jGTNVCMOZiKiq0tKAsDDdyl+Zmbr7mJVKwNOTzV9UKQxnIiIiI8OGMCIiIiPDcCYiIjIyDGciIiIjw3AmIiIyMgxnIiIiI8NwJiIiMjIMZyIiIiPDcCYiIjIyDGciIiIjw3AmIiIyMgxnIiIiI8NwJiIiMjIMZyIiIiPDcCYiIjIyDGciIiIjw3AmIiIyMgxnIiIiI8NwJiIiMjIMZyIiIiPDcCYiIjIyDGciIiIjw3AmIiIyMgxnIiIiI8NwJiIiMjIMZyIiIiPDcCYiIjIyDGciIiIjw3AmIiIyMgxnIiIiI8NwJiIiMjL/D0vXwuu0cjH2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#small graphs\n",
    "petersen=nx.petersen_graph()\n",
    "tutte=nx.tutte_graph()\n",
    "maze=nx.sedgewick_maze_graph()\n",
    "tet=nx.tetrahedral_graph()\n",
    "\n",
    "nx.draw(petersen)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a (constructive) generator for a classic graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "K_5=nx.complete_graph(5)\n",
    "K_3_5=nx.complete_bipartite_graph(3,5)\n",
    "barbell=nx.barbell_graph(10,10)\n",
    "lollipop=nx.lollipop_graph(10,20)\n",
    "nx.draw(K_3_5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a stochastic graph generator, e.g."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "er=nx.erdos_renyi_graph(100,0.15)\n",
    "ws=nx.watts_strogatz_graph(30,3,0.1)\n",
    "ba=nx.barabasi_albert_graph(100,5)\n",
    "red=nx.random_lobster(100,0.9,0.9)\n",
    "nx.draw(ba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
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   },
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